Data cleaning and pre-processing

This section shows the main steps that have been applied to pre-process the raw data.

aCDOM spectra

  • The CDOM spectra were modelled according to the information in Babin (2003).

    • acdom spectra were re-fitted using the complete data (i.e. between 350-500 nm) because the data in all_abs_transpose.txt started at 380 nm.

  • Average background values calculated between 683-687 nm and subtracted from each spectrum.

  • Some files were in binary format, so I could not open them (ex.: C2001000.YSA).

  • Some spectra start at 300 nm while others at 350 nm.

  • Calculated the correlation between the measured and the fitted values.

    • Fits with R2 than 0.95 were removed from the data.

  • Absorption spectra with any negative values below 500 nm were removed.

  • Exported the complete spectra (350-700 nm): both the raw and the modelled data.

Phytoplankton and non-algal absorption

  • Absorption spectra with any negative values below 500 nm were removed.

Irradiance

  • There were negative values in the irradiance data (Ed, Eu, Kd, Ku). I have cleaned the data by setting these negative values to NA.

This graph shows the number of negative values for Ed by wavelength.

  • Example of a spectral profile with negative values.

  • Eu is in fact Eu0- that was estimated using a two-exponential function model.

  • Ed is in fact Ed0- calculated from 0.94 x ed0+.

Reflectance

  • Reflectance values outside the 0-1 range were set to NA.

AC9

  • Negative values in a, c, bp, a_dissolved and c_dissolved have been set to NA.

  • a(715) was used as the baseline, that is why the values are always at 0 (see next graph).

Other stuff

  • Extracted extra variables (DOC, AQY) from Massimo 2000.

Visualizations

Just some graphs to visualize the data. Note that the same colour palette will be used to represent the areas in all graphics.

Temporal sampling

  • This graph shows when the sampling was performed in the different areas. For instance, we can see that a large fraction of the measurements were made in September of 1998.

Geographical map

There is a total of 424 different stations were sampled during the COASTLOOC expeditions.

  • Note that there are two stations without geographical coordinates: C2001000, C2002000.

Land-to-sea gradient

We could also present the data in relation with its distance to the land to get an overview of the landscape on optical quantities. For instance, acdom will likely be higher for stations located close to the shore because of the terrestrial influence.

Available variables

This graph shows an overview of the available variables (excluding radiometric measurements).

Absorption measurements

Overview of the averaged absorption spectra for each area.

Comparing acdom443 for the different areas shows that there is a clear open to coastal gradient.

We can see that the DOC follows the same pattern as acdom443.

We can also use scatter plots to further explore the relationships among variables.

Relationships between some pigments.

aphy

We could also assess the goodness of the relationships between total chlorophyll-a and phytoplankton absorption for each region.

anap

atot

acdom

Absorption partition

In this section I am using the same three stations as in Oubelkheir et al. (2007) to explore the additive contributions of each type of absorption.

  • For station C6024000, a_p is lower than a_cdom around 400 nm and 550 nm. Should we use this to filter out problematic spectra?

Irradiance

Ed

Eu

Kd

Ku

Reflectance

AC9

Absorption

Beam attenuation

Backscattering

Data validation and diagnostics

In this section, we will explore various diagnostic graphics to validate the data.

Absorption (AC9 and spectrofluorometer)

In theory, the dissolved absorption measured by the spectrofluorometer aCDOM should be comparable with the dissolved absorption measured by the AC9 (using a filter). The next graphs compare both measurements at common wavelengths for the different areas.

We can also use sina plot as another way to compare the same data.

Orientation of the paper

  • The data is a mix of temporal and spatial observations, so how should we present the data?

  • By area?

TODOS

  • No absorption for Med. Sea (Case 1). Is it normal?

  • There are a lot of nutrient parameters that have values of zero. Are they true zero or they indicate missing values?

  • There are wavelength gaps in the AC9, irradiance and reflectance data. Is that normal?

  • Calculate s_nap and s_cdom. See the method in Babin where he removes some wavelengths to calculate s_nap.

  • test

References

Babin, Marcel. 2003. “Variations in the Light Absorption Coefficients of Phytoplankton, Nonalgal Particles, and Dissolved Organic Matter in Coastal Waters Around Europe.” Journal of Geophysical Research 108 (C7): 3211. https://doi.org/10.1029/2001JC000882.
Oubelkheir, Kadija, Hervé Claustre, Annick Bricaud, and Marcel Babin. 2007. “Partitioning Total Spectral Absorption in Phytoplankton and Colored Detrital Material Contributions.” Limnology and Oceanography: Methods 5 (11): 384–95. https://doi.org/10.4319/lom.2007.5.384.